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Message queues allow services to communicate asynchronously, improving scalability and reliability.

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.

Poison messages in Kafka can cause infinite retries, risking system stability.

Kafka uses state stores to remember previous events in stateful stream processing.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.

Kafka can help ensure messages are processed once, but requires careful design.

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

Microservices can communicate using five key patterns based on their needs.

Message delivery semantics define how messages are sent in distributed systems and handle failures.

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

Kafka uses partitions to allow multiple consumers to process messages in parallel.

Older offsets can overwrite newer ones in Kafka if not handled properly.

Kafka consumers have three important states: Position, Committed Offset, and Application State.

Kafka consumer reprocessing involves safely replaying events to correct system states.

Kafka commit strategies determine how offsets are managed during message processing.

Kafka maintains message order within partitions using keys for routing.

Classifying failures and using controlled retries in Kafka prevents outages.

Kafka's `seek()` lets you replay only specific messages, saving time.

Kafka uses partitions to allow parallel processing while ensuring message order with keys.

Kafka's parallel processing depends on partitions, not just consumers or threads.

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

Kafka consumers can pause and resume message processing to handle backpressure effectively.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Kafka consumer offsets help track message processing to avoid loss and duplication.

Tokenization and analyzers help search engines process and match text effectively.

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

Backpressure prevents system overload by managing message processing rates in Kafka.

Kafka consumer lag shows how far behind consumers are from new messages.